{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SEIRS+ Deterministic Model Demo\n",
    "\n",
    "**This notebook provides a demonstration of the core functionality of the SEIRS+ Graph Model and offers a sandbox for easily changing simulation parameters and scenarios.** \n",
    "For a more thorough walkthrough of the model and use of this package, refer to the README."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing and Importing the model code\n",
    "All of the code needed to run the model is imported from the ```models``` module of this package.\n",
    "\n",
    "#### Install the package using ```pip```\n",
    "The package can be installed on your machine by entering this in the command line:\n",
    "\n",
    "```sudo pip install seirsplus```\n",
    "\n",
    "Then, the ```models``` module can be imported into your scripts as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from seirsplus.models import *\n",
    "import networkx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Alternatively, manually copy the code to your machine*\n",
    "*You can use the model code without installing a package by copying the ```models.py``` module file to a directory on your machine. In this case, the easiest way to use the module is to place your scripts in the same directory as the module, and import the module as shown here:*\n",
    "```python\n",
    "from models import *\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initializing the model parameters\n",
    "All model parameter values are set in the call to the ```SEIRSModel``` constructor. The basic SEIR parameters ```beta```, ```sigma```, ```gamma```, and ```initN``` are the only required arguments. All other arguments represent parameters for optional extended model dynamics; these optional parameters take default values that turn off their corresponding dynamics when not provided in the constructor. For clarity and ease of customization in this notebook, all available model parameters are listed below. \n",
    "\n",
    "For more information on parameter meanings, see the README.\n",
    "\n",
    "*The parameter values shown correspond to rough estimates of parameter values for the COVID-19 epidemic.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SEIRSModel(initN   =1000000,\n",
    "                   beta    =0.147, \n",
    "                   sigma   =1/5.2, \n",
    "                   gamma   =1/12.39, \n",
    "                   mu_I    =0.0004,\n",
    "                   mu_0    =0, \n",
    "                   nu      =0, \n",
    "                   xi      =0,\n",
    "                   beta_D  =0.147, \n",
    "                   sigma_D =1/5.2, \n",
    "                   gamma_D =1/12.39, \n",
    "                   mu_D    =0.0004,\n",
    "                   theta_E =0, \n",
    "                   theta_I =0, \n",
    "                   psi_E   =1.0, \n",
    "                   psi_I   =1.0,\n",
    "                   initI   =10000, \n",
    "                   initE   =0, \n",
    "                   initD_E =0, \n",
    "                   initD_I =0, \n",
    "                   initR   =0, \n",
    "                   initF   =0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checkpoints\n",
    "Model parameters can be easily changed during a simulation run using checkpoints. A dictionary holds a list of checkpoint times (```checkpoints['t']```) and lists of new values to assign to various model parameters at each checkpoint time. Any model parameter listed in the model constrcutor can be updated in this way. Only model parameters that are included in the checkpoints dictionary have their values updated at the checkpoint times, all other parameters keep their pre-existing values.\n",
    "\n",
    "*The checkpoints shown here correspond to starting social distancing (transmission rate ```beta``` reduced) and testing at time ```t=20``` (testing rates ```theta_E``` and ```theta_I``` are set to non-zero values) and then stopping social distancing at time ```t=100``` (```beta``` returned to its \"normal\" value; testing params remain non-zero).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoints = {'t':       [50, 100], \n",
    "               'beta':    [0.12, 0.147], \n",
    "               'theta_E': [0.02, 0.02], \n",
    "               'theta_I': [0.02, 0.02]\n",
    "              }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Checkpoint: Updating parameters]\n",
      "t = 49.90\n",
      "[Checkpoint: Updating parameters]\n",
      "t = 99.90\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.run(T=300, checkpoints=checkpoints)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the results\n",
    "The ```SEIRSNetworkModel``` class has a ```plot()``` convenience function for plotting simulation results on a matplotlib axis. This function generates a line plot of the frequency of each model state in the population by default, but there are many optional arguments that can be used to customize the plot.\n",
    "\n",
    "The ```SEIRSNetworkModel``` class also has convenience functions for generating a full figure out of model simulation results (optionaly arguments can be provided to customize the plots generated by these functions). \n",
    "- ```figure_basic()``` calls the ```plot()``` function with default parameters to generate a line plot of the frequency of each state in the population.\n",
    "- ```figure_infections()``` calls the ```plot()``` function with default parameters to generate a stacked area plot of the frequency of only the infection states ($E$, $I$, $D_E$, $D_I$) in the population.\n",
    "\n",
    "For more information on the built-in plotting functions, see the README."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAHhCAYAAABdpWmHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB12FLV5Pf0B1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122e51650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.figure_infections(vlines=checkpoints['t'], ylim=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reference simulation visualizations\n",
    "\n",
    "We can also visualize the results of another simulation(s) as a reference for comparison of our main simulation.\n",
    "\n",
    "Here we simulate a model where no distancing or testing takes place, so that we can compare the effects of these interventions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = 299.90\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_model = SEIRSModel(beta=0.147, sigma=1/5.2, gamma=1/12.39, mu_I=0.0004, initI=10000, initN=1000000) \n",
    "ref_model.run(T=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can visualize our main simulation together with this reference simulation by passing the model object of the reference simulation to the appropriate figure function argument (note: a second reference simulation could also be visualized by passing it to the ```dashed_reference_results``` argument):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAHhCAYAAABdpWmHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nahD9P/ZemccEcUzQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12609b190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.figure_infections(vlines=checkpoints['t'], ylim=0.2, shaded_reference_results=ref_model)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
